code: def test_mfcc(self):
      batch_size = 2
      num_features = 13
      num_classes = 40
      input_length = 2
  
      model = Wav2Letter(
          num_classes=num_classes,
          input_type="mfcc",
          num_features=num_features,
      )
  
      x = torch.rand(batch_size, num_features, input_length)
      out = model(x)
  
      assert out.size() == (batch_size, num_classes, 2)
  
imports: import itertools
  from collections import namedtuple
  import torch
  from parameterized import parameterized
  from torchaudio.models import ConvTasNet, DeepSpeech, Wav2Letter, WaveRNN
  from torchaudio.models.wavernn import MelResNet, UpsampleNetwork
  from torchaudio_unittest import common_utils
  from torchaudio_unittest.common_utils import torch_script
label_desc: Use image node_id 2 for calling the TestWav2Letter obj's underlying member method code with example usage: obj.test_mfcc() without return types